Due to the climate-dependent nature of renewable energy sources (RESs)

solving the optimal power flow (OPF) problem in power systems that integrate RESs

such as photovoltaic (PV) units and wind turbines (WTs)

remains a significant challenge.To address this problem

this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem

a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm (ECFA)

is deployed.In the proposed OPF model

the power generated from RESs is considered a dependent variable

while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs

respectively.Although the Coulomb’s and Franklin’s law algorithm (CFA) offers some advantages

it underperforms on complex optimization tasks compared to SSA

BA

SCA

ABC

and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem.